Swing-Free Manoeuvre Controller for Rotorcraft Unmanned Aerial Vehicle Slung-Load System Using Echo State Networks

Vargas, Aldo; Ireland, Murray; Anderson, David J.

doi:10.14323/ijuseng.2015.3

Cited by 7 publications

(5 citation statements)

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“…Before discussing those results, we also highlight the proposals of physical RC systems designed to predict the trajectory of a flying drone [93,95]. Although the idea of using physical processes taking place in the surrounding environment of the drone were not expressed in Ref.…”

Section: Physical Reservoir Computing Using Fluid Flow Disturbancesmentioning

confidence: 99%

“…Although the idea of using physical processes taking place in the surrounding environment of the drone were not expressed in Ref. [93,95], the approach presented there can be extended to implement the concepts illustrated in Figure 1a, b. In this context, we remind that the discipline of fluid dynamics is concerned with the flow of both liquids and gases [96].…”

Section: Physical Reservoir Computing Using Fluid Flow Disturbancesmentioning

confidence: 99%

See 1 more Smart Citation

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Abbas,

Abdel-Ghani,

Maksymov

2024

Preprint

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Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

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Section: Physical Reservoir Computing Using Fluid Flow Disturbancesmentioning

confidence: 99%

Section: Physical Reservoir Computing Using Fluid Flow Disturbancesmentioning

confidence: 99%

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Abbas,

Abdel-Ghani,

Maksymov

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In this system, each UAV uses an ESN to optimize paths and learns transmission power at different locations. Besides, ESN can also be used to control rotorcraft UAVs, which outperforms linear models in robustness to disturbances [259]. In 2021, [260] proposed a method for controlling flapping-wing UAVs in different wind directions, where strain sensors are applied to measure the wind movements, and a physical RC is used as a classifier to recognize the wind stream from the sensor data (see Fig.…”

Section: Machinerymentioning

confidence: 99%

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Zhang

Vargas

2023

IEEE Access

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Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps lowdimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.

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“…Physical constraints require physical models and there are plenty of dynamic UAV models in the literature. 12,14,29 However, as the physical model is going to be placed inside of a numerical optimization cost function, model fidelity must be minimized to reduce computational overhead while retaining only the necessary physical constraints, that is, the physical model needs to be fit-for-purpose.…”

Section: System Modellingmentioning

confidence: 99%

“…As the purpose of this research is to assess algorithms for autonomously creating optimal trajectories for missing person search, realistic constraints must be applied to the optimization to ensure that any computed trajectories are feasible. Physical constraints require physical models and there are plenty of dynamic UAV models in the literature 12,14,29 . However, as the physical model is going to be placed inside of a numerical optimization cost function, model fidelity must be minimized to reduce computational overhead while retaining only the necessary physical constraints, that is, the physical model needs to be fit‐for‐purpose .…”

Section: System Modellingmentioning

confidence: 99%

Optimal path planning using psychological profiling in drone‐assisted missing person search

Ewers,

Anderson,

Thomson

2023

Adv Control Appl

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Search and rescue operations are all time‐sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real‐life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators.

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Swing-Free Manoeuvre Controller for Rotorcraft Unmanned Aerial Vehicle Slung-Load System Using Echo State Networks

Cited by 7 publications

References 0 publications

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Optimal path planning using psychological profiling in drone‐assisted missing person search

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